| --- |
| library_name: onnx |
| license: apache-2.0 |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - image-classification |
| pipeline_tag: image-classification |
| --- |
| |
|  |
|
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| # DINOv2: Optimized for AMD ROCm |
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| DINOv2 is a self-supervised Vision Transformer evaluated here as an ImageNet linear classifier (ViT-S/B/L/G with a 1000-class head). This repository packages evaluation and inference for image classification using **ONNX Runtime**, exported and validated for **AMD ROCm** (via the MIGraphX execution provider) so it runs efficiently on AMD GPUs, CPUs, and NPUs. |
|
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| This is based on the implementation of DINOv2 found [here](https://github.com/facebookresearch/dinov2). |
| This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [dinov2 AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/dinov2) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline). |
|
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| --- |
|
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| ## Task Overview |
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| **Task:** Image classification |
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| **Dataset:** ImageNet validation (~50,000 images, 1,000 classes) |
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| **Output metrics:** Top-1 accuracy, Top-5 accuracy |
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| > **Model variants:** Default is **base** (`dinov2_vitb14_lc`). Override with `MODEL_SIZE=small|base|large|giant` (hub names: `dinov2_vits14_lc`, `dinov2_vitb14_lc`, `dinov2_vitl14_lc`, `dinov2_vitg14_lc`). |
| |
| --- |
| |
| ## AMD ROCm Optimization |
| |
| This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**, as well as AMD CPUs. Key points: |
| |
| - Validated backends: **ONNX Runtime** across **CPU** (native CPU execution provider), **GPU** (MIGraphX execution provider, ROCm-based), and **NPU** (VitisAI execution provider). |
| - CPU execution path supported independently of GPU/NPU availability, for environments without ROCm-capable GPU or NPU hardware. |
| |
| | Runtime | Precision | Backend | Hardware | Notes | |
| |---|---|---|---|---| |
| | ONNX Runtime | FP32, FP16, BF16, INT8 | CPU Execution Provider | AMD CPU | INT8 available via runtime auto-quantization flags | |
| | ONNX Runtime | FP32, FP16, BF16, INT8 | MIGraphX Execution Provider (ROCm) | AMD GPU | First run may take longer due to graph compilation | |
| | ONNX Runtime | FP32, FP16, BF16, INT8 | VitisAI Execution Provider | AMD NPU (Ryzen AI) | INT8 requires AMD Quark calibration (`make quantize-npu-int8`) | |
| |
| --- |
| |
| ## Getting Started |
| |
| For setup instructions, evaluation scripts, and custom configuration options, see the [dinov2 on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/dinov2). |
| |
| --- |
| |
| ## Model Details |
| |
| **Model Type:** Self-supervised Vision Transformer (ViT) image classifier with a linear-probe head |
| |
| **Base Model:** `facebookresearch/dinov2` (DINOv2 ViT-B/14 with linear classification head) |
| |
| **Model Stats:** |
| - Model variant: base (default) — small, large, giant also supported |
| - Precision tested: FP32, FP16, BF16, INT8 |
| |
| --- |
| |
| ## Accuracy Pipeline |
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| Higher Top-1 accuracy means a larger fraction of images are classified with the correct label as the top prediction — 100% is perfect, 0% is chance-level for random guessing. Top-5 allows credit when the true class appears anywhere in the model's five highest-scoring labels; values above ~80% Top-1 on ImageNet val are considered strong for this linear-probe setup. |
| |
| ### Metrics Explained |
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| | Metric | Description | |
| |--------|-------------| |
| | Top-1 | Primary classification metric — the fraction of images where the highest-scoring class matches ground truth. Strictest single-label score; a wrong top prediction counts as a full miss even if the true class ranked second. | |
| | Top-5 | Fraction of images where the true class appears in the model's top five predictions. Looser than Top-1 and typically higher; useful when near-miss rankings still indicate the model recognized the object category. | |
| |
| --- |
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| ## Dig Deeper |
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| Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples? |
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| 📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/dinov2)** |
| |
| The GitHub repository includes: |
| - Makefile-driven benchmark, profile, and evaluation targets for CPU, GPU, and NPU |
| - Per-device precision configs (FP32/FP16/BF16/INT8) and ONNX export scripts |
| - ImageNet dataset staging and full-validation evaluation pipeline |
| - Additional DINOv2 model variants (small, large, giant) |
| |
